Background While there has been tremendous promise of immunotherapy in treating cancer, most patients do not respond to treatment.1 Biomarker development has grown as the field attempts to better select patients that may benefit from immunotherapy as well as to further understanding of effective use of immunotherapy in cancer.2–4 Here, we utilize a deep learning approach to leverage single-cell information from spatial immunohistochemistry data to query predictive immune signatures of response to immunotherapy.
Methods In this work, we adapt a previously described multiple instance learning (MIL) approach5–7 to analyze single cell tabular data in a supervised machine learning approach, allowing us to not only create predictive models of response but interrogate the specific correlates of response learned by the underlying machine learning model. We utilize this newly described MIL deep learning approach (figure 1a) to analyze single cell data obtained from multiplexed spatial immunohistochemistry data obtained from pre-treatment tumor samples in CheckMate 275, a phase 2 clinical trial of checkpoint inhibition in metastatic urothelial carcinoma, to predict response (via RECIST) in this cohort and reveal insights into an effective immune response at the single cell level and their spatial relationships.
Results Our model was most predictive of response (figure 1b, AUC = 0.81) when applied solely to cells in the extra-tumoral tissue (outside of the tumor bed). When looking at the predictive signature of response, we noted an association of the predictive signature in the extra-tumoral space to key immune markers including CD3/CD8, CD11b, CD68, DC-LAMP, and PDL1 (figure 2a,b), suggesting the importance of this immune signature’s presence in the extra-tumoral tissue as being predictive of response prior to the initiation of immunotherapy. Finally, we interrogated the spatial organization of these predictive cells. By quantifying the level of co-localization of predictive cells via Moran’s Index, we noted that the predictive signature was more co-localized within responders vs non-responder (figure 3a,b), and was an independent correlate of response (figure 3c), suggesting an effective immune response not only requires an immune infiltrated tumor but co-localization of these key immune cells in the extra-tumoral space.
Conclusions These findings highlight the utility of deep learning at the single cell level to identify predictive immune signatures of response and note that while the quantity of the immune infiltration is predictive of response, the spatial organization of this immune response is an independent correlate of response and a hallmark of clinical benefit.
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Ethics Approval CheckMate 275 (NCT02387996) is a BMS-sponsored, multi-center, institutional-review-board-approved, phase 2 single arm clinical trial of nivolumab in patients with metastatic or unresectable urothelial cancer who have progressed or recurred following treatment with a platinum agent.
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